DETAILED ACTION
Notice of Pre-AIA or AIA Status
In the present application, filed on or after March 16, 2013, claims 1, 5, 7-8, 13, and 17 have been considered and examined under the first inventor to file provisions of the AIA .
Respond to Applicant’s Arguments/Remarks
Applicant’s arguments, see Remarks, filed 11/27/2024, with respect to the rejection(s) of claims 1, 5, 7-8, 13, and 17, based solely on the claimed limitations as amended, have been fully considered but are moot because the arguments do not apply to the new combination of references including prior art being used in the current rejection (see below for detail) under new grounds of rejection, necessitated by amendment.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, 8, 13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tupin et al. (Tupin – WO 2015/103127 A1) in view of Ma et al. (Ma – US 2017/0188841 A1), Monkam et al. (Monkam – Detection and Classification of Pulmonary Nodules using Convolutional Neural Networks: A Survey), Menkes et al. (Menkes – US 2017/0208778 A1), and Trundle (Trundle – US 10,200,272 B1).
As to claim 1, Tupin discloses a computer-implemented method for monitoring pet activity, the method comprising:
receiving sensor data related to a pet from a wearable device coupled to a collar worn by the pet (Tupin: FIG. 1-2 the wearable device 101), wherein the wearable device comprises a sensor (Tupin: [0007], [0040], [0044]-[0046], [0077], [0080]-[0082], and FIG. 1-3 the data management system and the wearable device) configured to collect sensor data corresponding to activities associated with the pet (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: Wearable device 101 may further accelerometer providing the acceleration signal 210. The accelerometer may be used to report levels of specific activities of an animal. For example, readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc. The accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101);
determining, based on the sensor data, one or more health indicators of the pet (Tupin: [0007]-[0008], [0040], [0090]-[0091], [0093]-[0096], [0109], [0146], FIG. 1-2, FIG. 8-12, and FIG. 22);
performing a wellness assessment of the pet based on the one or more health indicators of the pet (Tupin: [0090]-[0096], [0109], [0146],[0148], [0151]-[0154], FIG. 1-2, FIG. 8-12, and FIG. 22: the combined data may lead to an inference that the animal 401 is suffering from kennel cough or bronchitis. Further, because in some embodiments the data will be time-stamped, an inference may be readily determined even though the sensor readings are coming from disparate sources (here, wearable device 101 and a mobile device). Although as described the analysis step 1210 is performed at the DMS 301, in other embodiments the analysis may be performed at the user's mobile device and/or the wearable device 101), wherein the one or more health indicators comprise a metric for licking, scratching, itching, walking, or sleeping by the pet (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: Wearable device 101 may further accelerometer providing the acceleration signal 210. The accelerometer may be used to report levels of specific activities of an animal. For example, readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc. The accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101);
transmitting the wellness assessment to the mobile device (Tupin: [0041], [0090]-[0091], [0217]-[0224], and FIG. 25-26: while described herein as being located remote from the wearable device, the DMS may be located on the owner's smartphone or located on the wearable device based on the respective processing power of smartphone and wearable device. In these alternative embodiments, the "DMS" is identified by its ability to receive content from sources other than the sensors of the wearable device and process that additionally received content for forwarding to the owner and/or veterinarian of the specific anima ), and
displaying the wellness assessment of the pet at the mobile device using a graphical user interface (Tupin: [0218]-[0224] and FIG. 25-26: For purposes of illustration, each of the graphical displays of these items is shown as a dial with an arrow pivoting from one side of the dial to the other based on the state of the displayed item (e.g., a green area indicating no concern, a yellow area indicating caution, and a red area indicating concern for that individual item)).
While Tupin discloses a method of determining a health condition of an animal based on various sources of sensing/observation information using various known techniques including Bayesian inference analysis, neural networks, regression analysis, etc (Tupin: [0104], FIG. 8-12, and FIG. 22) , Tupin does not explicitly disclose
detecting a network condition associated with a network that the wearable device is connected to, wherein the wearable device communicates with a mobile device via the network;
transmitting the wellness assessment comprising particular content in a particular format to the mobile device, wherein the particular content and the particular format are determined based on the network condition; and
wherein the determining further comprises:
transforming the sensor data using symmetry different positions of the sensors with respect to the collar worn by the pet;
processing the transformed sensor data via an activity recognition model trained to analyze the pet’s moment-to-moment activities; and
determining the one or more health indicators based on an output of the activity recognition model.
However, it has been known in the art of monitoring conditions of a user to implement wherein the determining further comprises:
transforming the sensor data based on symmetry associated with the sensor data, wherein the symmetry is based on at least in part on one or more positions of wearable device on the pet, as suggested by Ma, which discloses wherein the determining further comprises:
transforming the sensor data based on symmetry associated with the sensor data, wherein the symmetry is based on at least in part on one or more positions of wearable device on the pet (Ma: Abstract, [0021], [0037], [0056], [0062]-[0066], and FIG. 1-8: the application can select a general or location-specific threshold difference based on the user's activity level. For example, during a monitoring period, the application can collect motion data through sensors integrated into the left and/or right socks or integrated into the computing device and then transform these motion data into an activity level of the user during the monitoring period. In this example, the application can adjust the threshold difference directly proportional to the user's activity level, such as by shifting the threshold difference along a spectrum between 1.5° C. and 3.5° C. proportional to the user's activity level. In this implementation, the application can also persist a threshold difference over a dwell period (e.g., thirty minutes) following a decrease in the user's activity level).
Therefore, in view of teachings by Turpin and Ma, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to implement in the data management system of Tupin to include wherein the determining further comprises: transforming the sensor data based on symmetry associated with the sensor data, wherein the symmetry is based on at least in part on one or more positions of wearable device on the pet, as suggested by Ma. The motivation for this is to implement a known alternative models for learning/detecting conditions/activities of a user based on sensing information.
While the combination of Turpin and Ma discloses a method of determining a health condition of an animal based on various sources of sensing/observation information using various known techniques including Bayesian inference analysis, neural networks, regression analysis, etc. (Tupin: [0104], FIG. 8-12, and FIG. 22) trained to analyze the pet’s moment-to-moment activities (Tupin: [0057], [0064], [0095], [0109], [0113]-[0115], [0120]-[0122], [0134], and FIG. 1: Wearable device 101 may further accelerometer providing the acceleration signal 210. The accelerometer may be used to report levels of specific activities of an animal. For example, readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc. The accelerometer may also be used to report the possibility of a high impact event as well as corroborate and/or augment other sensor readings. In some embodiments, the accelerometer may be used to control other sensors (e.g., turn on, turn off, leave a breadcrumb, ignore a reading, etc.). Further, the accelerometer may be used to determine which of a plurality of animals is actually wearing the wearable device 101), the combination of Turpin and Ma does not explicitly disclose wherein the determining further comprises:
processing the transformed sensor data via an activity recognition model; and
determining the one or more health indicators based on an output of the activity recognition model.
However, it has been known in the art of monitoring conditions of an animal to implement wherein the determining further comprises:
processing the transformed sensor data via an activity recognition model; and
determining the one or more health indicators based on an output of the activity recognition mode, as suggested by Monkam, which discloses wherein the determining further comprises:
processing the transformed sensor data via an activity recognition model; and
determining the one or more health indicators based on an output of the activity recognition mode (Monkam – Detection and Classification of Pulmonary Nodules using Convolutional Neural Networks: a Survey: With regard to the fact that the amount of contextual information is of great significance for detecting abnormalities from images and given that the fusion of multiple sources of image information may improve the detection performance, multi-stream CNN models also known as multiple path networks have been proposed (Fig. 2(c)) ).
Therefore, in view of teachings by Tupin, Ma, and Monkam, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to implement in the data management system of Tupin and Ma to include wherein the determining further comprises:
processing the transformed sensor data via an activity recognition model; and
determining the one or more health indicators based on an output of the activity recognition mode, as suggested by Monkam. The motivation for this is to implement a known alternative techniques for determining a status of an animal based on sensing information.
The combination of Tupin, Ma, and Monkam does not explicitly disclose transforming the sensor data using symmetry different positions of the sensors with respect to the collar worn by the pet.
However, it has been known in the art of monitoring conditions of an animal to implement transforming the sensor data using symmetry different positions of the sensors with respect to the collar worn by the pet, as suggested by Menkes, which discloses determining, based on the sensor data, one or more health indicators of the pet (Menkes: Abstract, [0038], [0041], [0046]-[0047], [0051], [0059], [0098], [0177], and FIG. 1-3: n system 11, the one or more processors 40, 40A may be configured to combine the identifying of the abnormal pattern in the at least one bioparameter with identifying abnormal patterns in at least one other bioparameter. For example, the identifying of the abnormal pattern in the at least one bioparameter involves identifying said abnormal patterns in at least one accelerometer-measured bioparameter and identifying abnormal patterns in at least one non-accelerometer-measured bioparameter);
performing a wellness assessment of the pet based on the one or more health indicators of the pet, wherein the one or more health indicators comprise a metric for licking, scratching, itching, walking, or sleeping by the pet (Menkes: Abstract, [0007]-[0008], [0035], [0041], [0059], [0063]-[0072],[0088], and FIG. 1-3: A system for monitoring vital signs of a pet animal comprises an annular band, an accelerometer configured to measure at least one of resting patterns, activity patterns, movement patterns, position patterns relating to, for example the pet animal relieving itself, lameness and scratching, and a non-accelerometer sensor configured to measure at least one of the following non-accelerometer-measured bioparameters of the pet animal: temperature, pulse rate, respiration rate );
wherein the determining further comprises:
transforming the sensor data using symmetry different positions of the sensors (Menkes: Abstract, [0063]-[0072], [0076], [0088], and FIG. 1-3: The sensor array 30 may also include a gyroscope 30d for capturing the vertical and/or horizontal movement of the pet. In the case of dogs, there are numerous basic dog postures that provide information as to what the dog is doing and thereby assist in interpreting vital sign measurements to arrive at a tentative diagnosis. The following basic dog postures that may be detected by sensor elements 30, for example a gyroscope, an accelerometer and/or a magnetometer: lying down laterally or right or left sides, lying down sternally (head up/down), lying on back, sitting, standing on four legs, standing on back legs, jumping, trotting, running, eating/drinking, urinating (male/female), defecating, limping hind leg, limping front leg, scratching hind leg, shaking leg, turning to lick, and stretching. The processor 40 make receive this information from the sensors 30 and utilize it in reaching a conclusion that it transmits remotely to the appropriate destination), with respect to the collar worn by the pet (Menkes: Abstract, [0063]-[0072], [0088], and FIG. 1-3: If there are two sensors elements, then the sensors 30 may be connected in parallel electrically (the at least one sensor element 30 may comprise two physically separated sensors connected electrically). One can also define the two sensor elements 30 as one distributed sensor element. Positioning two sensors 30 on the two sides of the neck of the animal may provide a guaranteed contact with the body regardless of movement or position… The sensor array 30 may also include a gyroscope 30d for capturing the vertical and/or horizontal movement of the pet. In the case of dogs, there are numerous basic dog postures that provide information as to what the dog is doing and thereby assist in interpreting vital sign measurements to arrive at a tentative diagnosis. The following basic dog postures that may be detected by sensor elements 30, for example a gyroscope, an accelerometer and/or a magnetometer: lying down laterally or right or left sides, lying down sternally (head up/down), lying on back, sitting, standing on four legs, standing on back legs, jumping, trotting, running, eating/drinking, urinating (male/female), defecating, limping hind leg, limping front leg, scratching hind leg, shaking leg, turning to lick, and stretching. The processor 40 make receive this information from the sensors 30 and utilize it in reaching a conclusion that it transmits remotely to the appropriate destination).
Therefore, in view of teachings by Tupin, Ma, Monkam, and Menkes it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to implement in the data management system of Tupin, Ma, and Monkam to include transforming the sensor data using symmetry different positions of the sensors with respect to the collar worn by the pet, as suggested by Menkes. The motivation for this is to implement a known alternative techniques for determining a status of an animal based on sensing information.
The combination of Tupin, Ma, Monkam, and Menkes discloses a system/method for transmitting the wellness assessment of the pet to the mobile device based on sensors from a wearable device (Tupin: [0041], [0090]-[0091], [0217]-[0224], and FIG. 25-26: while described herein as being located remote from the wearable device, the DMS may be located on the owner's smartphone or located on the wearable device based on the respective processing power of smartphone and wearable device. In these alternative embodiments, the "DMS" is identified by its ability to receive content from sources other than the sensors of the wearable device and process that additionally received content for forwarding to the owner and/or veterinarian of the specific animal); the combination of Tupin, Ma, Monkam, and Menkes does not explicitly disclose detecting a network condition associated with a network that the wearable device is connected to, wherein the wearable device communicates with a mobile device via the network; and
transmitting the information comprising particular content in a particular format to the device, wherein the particular content and the particular format are determined based on the network condition.
However, it has been known in the art of network communication to implement detecting a network condition associated with a network that the wearable device is connected to, wherein the wearable device communicates with a mobile device via the network; and transmitting the information comprising particular content in a particular format to the device, wherein the particular content and the particular format are determined based on the network condition, as suggested by Trundle, which discloses detecting a network condition associated with a network that the wearable device is connected to, wherein the wearable device communicates with a mobile device via the network (Trundle: Abstract, column 2 lines 43-57, column 3 lines 64 – column 4 lines 38, column 5 lines 6-49, column 7 lines 33-51, column 8 lines 63-column 9 lines 46, FIG. 1-3, and FIG. 5-6: the network profile 124 may be used to distinguish between different types of data generated over the local network 105. For instance, in such implementations, the network profile 124 may include information related to the data to be transmitted over the carrier network 107 such as latency (e.g., time importance) of data, or the size of the data transmission payload. Information included in the network profile 124 may be used to determine if data generated by the cameras 130 or the client devices 140 should presently be transmitted over the carrier network 107. For example, the network profile 124 may indicate that video footage collected from cameras 130 that triggered an alarm event is high latency data because the data relates to security services that require an immediate transmission. In another example, the network profile 124 may indicate that internet activity data from client devices 140 is low latency data but have a small enough data transmission payload to be transmitted immediately without significantly impacting the bandwidth over the carrier network 107); and
transmitting the information comprising particular content in a particular format to the device, wherein the particular content and the particular format are determined based on the network condition (Trundle: Abstract, column 2 lines 43-57, column 3 lines 64 – column 4 lines 38, column 5 lines 6-49, column 7 lines 33-51, column 8 lines 63-column 9 lines 46, FIG. 1-3, and FIG. 5-6: In some instances, if the data to be transmitted has a significant file size that exceeds the size indicated by the network characteristics, but the data to be transmitted is identified as including highly priority information, then the data may still be transmitted regardless of the file size exceeds a recommended value because the data priority characteristic has a larger weight compared to the file size characteristic of the data to be transmitted. In other examples, if the data to be transmitted has a relatively small size that may not impact the throughput over the carrier network 107, as indicated by the network characteristics, but is not designated as high priority data, then data transmission may be deferred because the current network characteristics indicate that the present throughput of the carrier network 107 is sufficiently high to make the present transmission prohibitively expensive).
Therefore, in view of teachings by Tupin, Ma, Monkam, Menkes and Trundle, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to implement in the data management system of Tupin, Ma, Monkam, and Menkes to include detecting a network condition associated with a network that the wearable device is connected to, wherein the wearable device communicates with a mobile device via the network; and
transmitting the information comprising particular content in a particular format to the device, wherein the particular content and the particular format are determined based on the network condition, as suggested by Trundle. The motivation for this is to selectively transmit priority information based on a state of a communication network.
As to claim 5, Tupin, Ma, Monkam, Menkes and Trundle disclose the limitations of claim 1 further comprising the method according to claim 1, wherein the activity recognition model is a deep neural network comprising two or more layer modules, wherein each of the layer modules includes at least one of a many-to-many approach, striding, downsampling, pooling, multi-scaling, or batch normalization (Monkam – Detection and Classification of Pulmonary Nodules using Convolutional Neural Networks: a Survey: With regard to the fact that the amount of contextual information is of great significance for detecting abnormalities from images and given that the fusion of multiple sources of image information may improve the detection performance, multi-stream CNN models also known as multiple path networks have been proposed (Fig. 2(c))).
As to claim 8, Tupin, Ma, Monkam, Menkes and Trundle discloses the limitations of claim 1 further comprising the method according to claim 1, further comprising:
transmitting a request to a pet owner or caregiver to provide feedback on the one or more health indicators of the pet (Tupin: [0079], [0081], [0093]-[0094], and [0105]: Each of these external sensors and/or mobile browser applications/installed applications may act independently, in conjunction with the wearable device 101, may be triggered by the wearable device 101, or may be triggered by the DMS on a demand, episodic, or a scheduled basis to provide additional and/or collaborative sensing information that will provide important episodic, derived, or trending information to support the animals safety, wellbeing and health);
receiving the feedback from the pet owner or caregiver (Tupin: [0040], [0081], [0093-[0096], [0109], [0151], and FIG. 3); and
updating the activity recognition model based on the feedback from the pet owner or caregiver (Tupin: [0104], FIG. 8-12, and FIG. 22 and Monkam – Detection and Classification of Pulmonary Nodules using Convolutional Neural Networks: a Survey: With regard to the fact that the amount of contextual information is of great significance for detecting abnormalities from images and given that the fusion of multiple sources of image information may improve the detection performance, multi-stream CNN models also known as multiple path networks have been proposed (Fig. 2(c))).
As to claim 13, Tupin, Ma, Monkam, Menkes and Trundle discloses the limitations of claim 1 further comprising the method according to claim 1, wherein the sensor data comprises a location of the pet, wherein the location is determined using a global positioning system (Tupin: [0043]-[0044], [0054]-[0059], and FIG. 1-2 the GPS 106: The GPS receiver 106 may provide any useful information regarding the status of an animal wearing wearable device 101 including location coordinates of the animal, elevation of the animal, specific satellite acquisition status, and the orientation of satellites. Some or all of this information may be used in sensor logic calculations and reduce GPS thrashing ( continuous attempts to acquire signals and thereby draining the battery) ).
As to claim 17, Tupin, Ma, Monkam, Menkes and Trundle discloses the limitations of claim 1 further comprising the method according to claim 1, further comprising:
determining a health recommendation or fitness nudge for the pet based on the wellness assessment (Tupin: [0090]-[0096], [0109], [0146],[0148], [0151]-[0154], FIG. 1-2, FIG. 8-12, and FIG. 22: the combined data may lead to an inference that the animal 401 is suffering from kennel cough or bronchitis. Further, because in some embodiments the data will be time-stamped, an inference may be readily determined even though the sensor readings are coming from disparate sources (here, wearable device 101 and a mobile device). Although as described the analysis step 1210 is performed at the DMS 301, in other embodiments the analysis may be performed at the user's mobile device and/or the wearable device 101); and
transmitting the health recommendation or fitness nudge to the mobile device (Tupin: [0041], [0090]-[0091], [0218]-[0224] and FIG. 25-26: For purposes of illustration, each of the graphical displays of these items is shown as a dial with an arrow pivoting from one side of the dial to the other based on the state of the displayed item (e.g., a green area indicating no concern, a yellow area indicating caution, and a red area indicating concern for that individual item)).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Tupin et al. (Tupin – WO 2015/103127 A1) in view of Ma et al. (Ma – US 2017/0188841 A1), Monkam et al. (Monkam – Detection and Classification of Pulmonary Nodules using Convolutional Neural Networks: A Survey), Menkes et al. (Menkes – US 2017/0208778 A1), and Trundle (Trundle – US 10,200,272 B1) and further in view of Arden Dertat (Dertat – Applied Deep Learning – Part 4 Convolutional Neural Networks).
As to claim 7, While the combination of Tupin, Ma, Monkam, Menkes and Trundle discloses the limitations of claim 5, the combination of Tupin, Ma, Monkam, Menkes and Trundle further discloses a method of determining a health condition of an animal based on various sources of sensing/observation information using various known techniques including Bayesian inference analysis, neural networks, regression analysis, etc (Tupin: [0104], FIG. 8-12, and FIG. 22) using convolutional neural networks (Monkam – Detection and Classification of Pulmonary Nodules using Convolutional Neural Networks: a Survey: With regard to the fact that the amount of contextual information is of great significance for detecting abnormalities from images and given that the fusion of multiple sources of image information may improve the detection performance, multi-stream CNN models also known as multiple path networks have been proposed (Fig. 2(c))), except for the claimed limitations of the method according to claim 5, wherein each of the layer modules can be represented as: FLMtype(Wout, S,K, Ddrop, bBN), where the type is a convolutional neural network (CNN), Wow is a number of output channels, s is a stride ratio, k is a kernel length, Pdrop is a dropout probability, and bBN, is a batch normalization.
However, it has been known in the art of neural networks to implement wherein each of the layer modules can be represented as: FLMtype(Wout, S,K, Ddrop, bBN), where the type is a convolutional neural network (CNN), Wow is a number of output channels, s is a stride ratio, k is a kernel length, Pdrop is a dropout probability, and bBN, is a batch normalization, as suggested by Dertat, which discloses wherein each of the layer modules can be represented as: FLMtype(Wout, S,K, Ddrop, bBN), where the type is a convolutional neural network (CNN), Wow is a number of output channels, s is a stride ratio, k is a kernel length, Pdrop is a dropout probability, and bBN, is a batch normalization (Dertat – Applied Deep Learning – Part 4 Convolutional Neural networks).
Therefore, in view of teachings by Tupin, Ma, Monkam, Menkes, Trundle, and Dertat it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to implement in the data management system of Tupin, Ma, Monkam, Menkes, and Trundle to include wherein each of the layer modules can be represented as: FLMtype(Wout, S,K, Ddrop, bBN), where the type is a convolutional neural network (CNN), Wow is a number of output channels, s is a stride ratio, k is a kernel length, Pdrop is a dropout probability, and bBN, is a batch normalization, as suggested by Dertat. The motivation for this is to implement a known alternative models/techniques of learning machines.
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Fu et al., US 11,245,633 B2, discloses method and wireless device for handling transmission of data.
Kavathekar, US 11,050,581 B1, discloses adaptive supervision signals.
Narsude et al., US 10,505,817 B2, discloses automatically determining an optimal amount of tie for analyzing a distributed network environment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP §706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUANG PHAM whose telephone number is (571)-270-3668. The examiner can normally be reached on Monday - Thursday 9:30 AM - 5:00 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, QUAN-ZHEN WANG can be reached on (571)-272-3114. The fax phone number for the organization where this application or proceeding is assigned is (571)-273-8300.
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/QUANG PHAM/Primary Examiner, Art Unit 2685